correlation

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Correlation

Statistical measure of the degree to which the movements of two variables (stock/option/convertible prices or returns) are related. See: Correlation coefficient.

correlation

The relationship between two variables during a period of time, especially one that shows a close match between the variables' movements. For example, all utility stocks tend to have a high degree of correlation because their share prices are influenced by the same forces. Conversely, gold stock price movements are not closely correlated with utility stock price movements because the two are influenced by very different factors. The concept of correlation is frequently used in portfolio analysis. See also serial correlation.

Correlation.

In investment terms, correlation is the extent to which the values of different types of investments move in tandem with one another in response to changing economic and market conditions.

Correlation is measured on a scale of - 1 to +1. Investments with a correlation of + 0.5 or more tend to rise and fall in value at the same time. Investments with a negative correlation of - 0.5 to - 1 are more likely to gain or lose value in opposing cycles.

correlation

a statistical term that describes the degree of association between two variables. When two variables tend to change together, then they are said to be correlated, and the extent to which they are correlated is measured by means of the CORRELATION COEFFICIENT.

correlation

A former appraisal term, replaced by reconciliation.
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Now, Correlations is working to expand their network and add on to the site.
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This correlation decreases during extreme market circumstances, particularly during market crashes.
Excluding those void cell pairs that do not contain sufficient data points, the correlations were implemented for 266 cell pairs of 10 KM correlations and 4794 cell pairs of 180 KNM correlations.
As you can see from the chart, pair-wise correlation has historically hovered between 10% and 40%.
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The significance level indicates the reliability of the correlation and can change based on sample size.
This was done with SPSS by correlating the set of values against itself, and then the values are shifted one step backwards until the highest correlation coefficient was achieved.
Fourier correlation process is used for function discrimination.
Shammasi's correlation for oil formation volume factor, the new correlation is evaluated by doing regression analysis.
Figure 8 shows the user interface, an example of numerical output and a partial visual representation of a Factor Analysis obtained from a tetrachoric correlations matrix.